1. Field of the Invention
The present invention generally relates to methods and systems for detecting defects on a specimen using a combination of bright field channel data and dark field channel data. Certain embodiments relate to combining pixel-level data acquired for a specimen by a bright field channel and a dark field channel of an inspection system and detecting defects on the specimen by applying a two-dimensional threshold to the combined data.
2. Description of the Related Art
The following description and examples are not admitted to be prior art by virtue of their inclusion in this section.
Fabricating semiconductor devices such as logic and memory devices typically includes processing a substrate such as a semiconductor wafer using a large number of semiconductor fabrication processes to form various features and multiple levels of the semiconductor devices. For example, lithography is a semiconductor fabrication process that involves transferring a pattern from a reticle to a resist arranged on a semiconductor wafer. Additional examples of semiconductor fabrication processes include, but are not limited to, chemical-mechanical polishing, etch, deposition, and ion implantation. Multiple semiconductor devices may be fabricated in an arrangement on a single semiconductor wafer and then separated into individual semiconductor devices.
Inspection processes are used at various steps during a semiconductor manufacturing process to detect defects on wafers to promote higher yield in the manufacturing process and thus higher profits. Inspection has always been an important part of fabricating semiconductor devices such as integrated circuits (ICs). However, as the dimensions of semiconductor devices decrease, inspection becomes even more important to the successful manufacture of acceptable semiconductor devices because smaller defects can cause the devices to fail. For instance, as the dimensions of semiconductor devices decrease, detection of defects of decreasing size has become necessary since even relatively small defects may cause unwanted aberrations in the semiconductor devices.
Inspection for many different types of defects has also become more important recently. For instance, in order to use inspection results to monitor and correct semiconductor fabrication processes, it is often necessary to know what types of defects are present on a specimen. In addition, since controlling every process involved in semiconductor manufacturing is desirable to attain the highest yield possible, it is desirable to have the capability to detect the different types of defects that may result from many different semiconductor processes. The different types of defects that are to be detected may vary dramatically in their characteristics. For example, defects that may be desirable to detect during a semiconductor manufacturing process may include thickness variations, particulate defects, scratches, pattern defects such as missing pattern features or incorrectly sized pattern features, and many others having such disparate characteristics.
In order for inspection to provide useful results for yield control, the inspection process must be able to not only detect many different kinds of defects but also to discriminate between real defects on the wafer or reticle and noise or nuisance events. Noise may be defined as events detected on a wafer or reticle by an inspection tool that are not actually defects but appear as potential defects due to marginalities in the inspection tool such as marginalities in data processing and/or data acquisition. Nuisance events are actual defects but that are not relevant to the user for the purposes of controlling the process or predicting yield. Moreover, the same defect may be considered a nuisance event at one point in time, but it may later be found to be a relevant defect. In some instances, the number of noise and nuisance events detected by an inspection tool can be reduced by using optimized data acquisition parameters and optimized data processing parameters. In addition, the number of noise and nuisance events can be reduced by applying various filtering techniques to the inspection results.
As design rules shrink, however, semiconductor manufacturing processes may be operating closer to the limitations on the performance capability of the processes. In addition, smaller defects can have an impact on the electrical characteristics of the device as the design rules shrink, which drives more sensitive inspections. Therefore, as design rules shrink, the population of potentially yield relevant defects detected by inspection grows dramatically, and the population of nuisance defects detected by inspection also increases dramatically. Consequently, more and more defects may be detected on the wafers, and correcting the manufacturing processes to eliminate all of the defects may be difficult and expensive. As such, determining which of the defects actually have an effect on the electrical characteristics of the devices and the yield may allow process control methods to be focused on those defects while largely ignoring others. Furthermore, at smaller design rules, process induced failures may, in some cases, tend to be systematic. That is, process induced failures tend to fail at predetermined design patterns often repeated many times within the design. Elimination of spatially systematic, electrically relevant defects is important because eliminating such defects can have a significant overall impact on yield.
Classifying defects found on wafers and other specimens has, therefore, become increasingly important in order to determine what kinds of defects are present on the wafers in addition to distinguishing the defect types of interest from other defect types. Several fully automatic defect classification (ADC) tools are now available. Typically, these tools use classification “recipes” to perform defect classification. A “recipe” can be generally defined as a set of instructions that define an operation to be performed by a tool and that are provided to and run on the tool upon request by a user. Classification recipes are typically generated using previous data acquired for specific defect classes that may be assembled in a suitable database. In the simplest implementation, the ADC tool can then compare unknown defects to those included in the specific defect classes to determine which defect class the unknown defect is most like. Obviously, much more complicated algorithms can be used by the ADC tool to determine which of the defect classes the unknown defect most likely belongs to.
Sometimes ADC is performed after inspection of a wafer. However, some systems and methods have been developed that can be used to perform ADC during inspection or “on-the-fly.” Examples of such systems and methods are illustrated in International Publication No. WO 99/67626 by Ravid et al., which is incorporated by reference as if fully set forth herein. The systems and methods described in this publication are generally configured to separately detect defects in the electrical signals produced by different detectors. In other words, the electrical signals produced by each of the detectors are processed separately to determine if each detector has detected a defect. At any time that a defect is detected in the electrical signals produced by one of the detectors, the electrical signals produced by at least two of the detectors are analyzed collectively to determine scattered light attributes of the defect such as reflected light intensity, reflected light volume, reflected light linearity, and reflected light asymmetry. The defect is then classified (e.g., as a pattern defect or a particle defect) based on these attributes.
Although the methods and systems disclosed in the above-referenced publication utilize scattered light attributes of defects determined from electrical signals generated by more than one detector, the methods and systems disclosed in this publication do not utilize electrical signals generated by more than one detector in combination to detect the defects. In addition, the methods and systems disclosed in this publication do not use a combination of electrical signals generated by more than one detector for any defect-related function other than classification. Other currently available inspection systems are configured to inspect a specimen with more than one detection channel, to detect defects on the specimen by separately processing the data acquired by each of the channels, and to classify the defects by separately processing the data acquired by each of the channels. The defects detected by each of the individual channels may also be further processed separately, for example, by generating different wafer maps, each illustrating the defects detected by only one of the individual channels. The results generated by more than one channel of such a system may then be combined using, for example, Venn addition of the individual wafer maps.
Accordingly, it would be advantageous to develop methods and systems that utilize data generated by more than one detection channel of an inspection system to detect defects on a specimen thereby increasing the signal-to-noise ratio of defect detection and/or to perform one or more other defect-related functions thereby increasing the sensitivity, accuracy, and/or precision of the defect-related functions.